upstream-archive/stable-diffusion.cpp
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feat: add verbose logging and log-level selection (#1941)

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leejet
2026-09-06 23:14:03 +08:00
committed by GitHub
parent 462d675018
commit dbb611264e
69 changed files with 551 additions and 484 deletions
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src/model/diffusion/anima.hpp
+5 -5
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@@ -46,11 +46,11 @@ namespace Anima {
}
if (detected_layers > 0) {
config.num_layers = detected_layers;
LOG_DEBUG("anima: num_layers = %" PRId64 ", hidden_size = %" PRId64 ", num_heads = %" PRId64 ", head_dim = %" PRId64,
config.num_layers,
config.hidden_size,
config.num_heads,
config.head_dim);
LOG_VERBOSE("anima: num_layers = %" PRId64 ", hidden_size = %" PRId64 ", num_heads = %" PRId64 ", head_dim = %" PRId64,
config.num_layers,
config.hidden_size,
config.num_heads,
config.head_dim);
}
return config;
}
src/model/diffusion/boogu.hpp
+10 -10
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@@ -109,16 +109,16 @@ namespace Boogu {
}
config.timestep_embed_dim = std::min<int64_t>(config.hidden_size, 1024);
LOG_DEBUG("boogu_image: layers=%" PRId64 ", double_stream_layers=%" PRId64 ", refiner_layers=%" PRId64 ", hidden=%" PRId64 ", heads=%" PRId64 ", kv_heads=%" PRId64 ", head_dim=%" PRId64 ", in_channels=%" PRId64 ", out_channels=%" PRId64,
config.num_layers,
config.num_double_stream_layers,
config.num_refiner_layers,
config.hidden_size,
config.num_attention_heads,
config.num_kv_heads,
config.head_dim,
config.in_channels,
config.out_channels);
LOG_VERBOSE("boogu_image: layers=%" PRId64 ", double_stream_layers=%" PRId64 ", refiner_layers=%" PRId64 ", hidden=%" PRId64 ", heads=%" PRId64 ", kv_heads=%" PRId64 ", head_dim=%" PRId64 ", in_channels=%" PRId64 ", out_channels=%" PRId64,
config.num_layers,
config.num_double_stream_layers,
config.num_refiner_layers,
config.hidden_size,
config.num_attention_heads,
config.num_kv_heads,
config.head_dim,
config.in_channels,
config.out_channels);
return config;
}
};
src/model/diffusion/ernie_image.hpp
+7 -7
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@@ -72,13 +72,13 @@ namespace ErnieImage {
for (int axis_dim : config.axes_dim) {
config.axes_dim_sum += axis_dim;
}
LOG_DEBUG("ernie_image: num_layers = %" PRId64 ", hidden_size = %" PRId64 ", num_heads = %" PRId64 ", ffn_hidden_size = %" PRId64 ", in_channels = %" PRId64 ", out_channels = %" PRId64,
config.num_layers,
config.hidden_size,
config.num_heads,
config.ffn_hidden_size,
config.in_channels,
config.out_channels);
LOG_VERBOSE("ernie_image: num_layers = %" PRId64 ", hidden_size = %" PRId64 ", num_heads = %" PRId64 ", ffn_hidden_size = %" PRId64 ", in_channels = %" PRId64 ", out_channels = %" PRId64,
config.num_layers,
config.hidden_size,
config.num_heads,
config.ffn_hidden_size,
config.in_channels,
config.out_channels);
return config;
}
};
src/model/diffusion/flux.hpp
+13 -13
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@@ -123,16 +123,16 @@ namespace Flux {
config.guidance_embed = true;
}
if (name.find("__x0__") != std::string::npos) {
LOG_DEBUG("using x0 prediction");
LOG_VERBOSE("using x0 prediction");
config.chroma_radiance_params.use_x0 = true;
}
if (name.find("__32x32__") != std::string::npos) {
LOG_DEBUG("using patch size 32");
LOG_VERBOSE("using patch size 32");
config.patch_size = 32;
}
if (name.find("img_in_patch.weight") != std::string::npos) {
actual_radiance_patch_size = tensor_storage.ne[0];
LOG_DEBUG("actual radiance patch size: %" PRId64, actual_radiance_patch_size);
LOG_VERBOSE("actual radiance patch size: %" PRId64, actual_radiance_patch_size);
}
if (name.find("distilled_guidance_layer.in_proj.weight") != std::string::npos) {
config.is_chroma = true;
@@ -169,7 +169,7 @@ namespace Flux {
}
if (actual_radiance_patch_size > 0 && actual_radiance_patch_size != config.patch_size) {
GGML_ASSERT(config.patch_size == 2 * actual_radiance_patch_size);
LOG_DEBUG("using fake x2 patch size");
LOG_VERBOSE("using fake x2 patch size");
config.chroma_radiance_params.fake_patch_size_x2 = true;
}
if (head_dim > 0) {
@@ -179,13 +179,13 @@ namespace Flux {
for (int axis_dim : config.axes_dim) {
config.axes_dim_sum += axis_dim;
}
LOG_DEBUG("flux: depth = %d, depth_single_blocks = %d, guidance_embed = %s, context_in_dim = %" PRId64 ", hidden_size = %" PRId64 ", num_heads = %d",
config.depth,
config.depth_single_blocks,
config.guidance_embed ? "true" : "false",
config.context_in_dim,
config.hidden_size,
config.num_heads);
LOG_VERBOSE("flux: depth = %d, depth_single_blocks = %d, guidance_embed = %s, context_in_dim = %" PRId64 ", hidden_size = %" PRId64 ", num_heads = %d",
config.depth,
config.depth_single_blocks,
config.guidance_embed ? "true" : "false",
config.context_in_dim,
config.hidden_size,
config.num_heads);
return config;
}
};
@@ -1560,7 +1560,7 @@ namespace Flux {
config.axes_dim,
sd_version_is_longcat(version));
int pos_len = static_cast<int>(pe_vec.size() / config.axes_dim_sum / 2);
// LOG_DEBUG("pos_len %d", pos_len);
// LOG_VERBOSE("pos_len %d", pos_len);
auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, config.axes_dim_sum / 2, pos_len);
// pe->data = pe_vec.data();
// print_ggml_tensor(pe);
@@ -1702,7 +1702,7 @@ namespace Flux {
GGML_ASSERT(!out_opt.empty());
out = std::move(out_opt);
print_sd_tensor(out);
LOG_DEBUG("flux test done in %lldms", t1 - t0);
LOG_VERBOSE("flux test done in %lldms", t1 - t0);
}
}
src/model/diffusion/hunyuan.hpp
+11 -11
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@@ -266,16 +266,16 @@ namespace Hunyuan {
GGML_ASSERT(config.hidden_size / config.num_heads == config.axes_dim_sum);
if (inferred) {
LOG_DEBUG("hunyuan video: depth = %d, single depth = %d, in_channels = %" PRId64 ", out_channels = %" PRId64 ", hidden_size = %" PRId64 ", context_in_dim = %" PRId64 ", patch_size = %dx%dx%d",
config.depth,
config.depth_single_blocks,
config.in_channels,
config.out_channels,
config.hidden_size,
config.context_in_dim,
std::get<0>(config.patch_size),
std::get<1>(config.patch_size),
std::get<2>(config.patch_size));
LOG_VERBOSE("hunyuan video: depth = %d, single depth = %d, in_channels = %" PRId64 ", out_channels = %" PRId64 ", hidden_size = %" PRId64 ", context_in_dim = %" PRId64 ", patch_size = %dx%dx%d",
config.depth,
config.depth_single_blocks,
config.in_channels,
config.out_channels,
config.hidden_size,
config.context_in_dim,
std::get<0>(config.patch_size),
std::get<1>(config.patch_size),
std::get<2>(config.patch_size));
}
return config;
}
@@ -615,7 +615,7 @@ namespace Hunyuan {
config.theta,
config.axes_dim);
int64_t pos_len = static_cast<int64_t>(pe_vec.size() / config.axes_dim_sum / 2);
// LOG_DEBUG("pos_len %d", pos_len);
// LOG_VERBOSE("pos_len %d", pos_len);
auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, config.axes_dim_sum / 2, pos_len);
// pe->data = pe_vec.data();
// print_ggml_tensor(pe, true, "pe");
src/model/diffusion/ideogram4.hpp
+6 -6
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@@ -58,11 +58,11 @@ namespace Ideogram4 {
}
if (detected_layers > 0) {
config.num_layers = detected_layers;
LOG_DEBUG("ideogram4: num_layers = %" PRId64 ", emb_dim = %" PRId64 ", num_heads = %" PRId64 ", intermediate_size = %" PRId64,
config.num_layers,
config.emb_dim,
config.num_heads,
config.intermediate_size);
LOG_VERBOSE("ideogram4: num_layers = %" PRId64 ", emb_dim = %" PRId64 ", num_heads = %" PRId64 ", intermediate_size = %" PRId64,
config.num_layers,
config.emb_dim,
config.num_heads,
config.intermediate_size);
}
return config;
}
@@ -465,7 +465,7 @@ namespace Ideogram4 {
}
}
if (has_uncond_model) {
LOG_DEBUG("using uncond model");
LOG_VERBOSE("using uncond model");
uncond_model = Ideogram4Transformer(config);
uncond_model.init(params_ctx, tensor_storage_map, uncond_prefix);
}
src/model/diffusion/krea2.hpp
+10 -10
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@@ -143,16 +143,16 @@ namespace Krea2 {
}
config.update_axes_dim();
LOG_DEBUG("krea2: layers=%" PRId64 ", features=%" PRId64 ", heads=%" PRId64 ", kv_heads=%" PRId64 ", text_dim=%" PRId64 ", text_layers=%" PRId64 ", text_heads=%" PRId64 ", text_kv_heads=%" PRId64 ", channels=%" PRId64,
config.layers,
config.features,
config.heads,
config.kv_heads,
config.text_dim,
config.text_layers,
config.text_heads,
config.text_kv_heads,
config.in_channels);
LOG_VERBOSE("krea2: layers=%" PRId64 ", features=%" PRId64 ", heads=%" PRId64 ", kv_heads=%" PRId64 ", text_dim=%" PRId64 ", text_layers=%" PRId64 ", text_heads=%" PRId64 ", text_kv_heads=%" PRId64 ", channels=%" PRId64,
config.layers,
config.features,
config.heads,
config.kv_heads,
config.text_dim,
config.text_layers,
config.text_heads,
config.text_kv_heads,
config.in_channels);
return config;
}
};
src/model/diffusion/lens.hpp
+8 -8
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@@ -66,14 +66,14 @@ namespace Lens {
for (int axis_dim : config.axes_dim) {
config.axes_dim_sum += axis_dim;
}
LOG_DEBUG("lens: num_layers = %d, selected_layer_count = %d, hidden_size = %" PRId64 ", num_attention_heads = %" PRId64 ", attention_head_dim = %" PRId64 ", in_channels = %" PRId64 ", out_channels = %" PRId64,
config.num_layers,
config.selected_layer_count,
config.num_attention_heads * config.attention_head_dim,
config.num_attention_heads,
config.attention_head_dim,
config.in_channels,
config.out_channels);
LOG_VERBOSE("lens: num_layers = %d, selected_layer_count = %d, hidden_size = %" PRId64 ", num_attention_heads = %" PRId64 ", attention_head_dim = %" PRId64 ", in_channels = %" PRId64 ", out_channels = %" PRId64,
config.num_layers,
config.selected_layer_count,
config.num_attention_heads * config.attention_head_dim,
config.num_attention_heads,
config.attention_head_dim,
config.in_channels,
config.out_channels);
return config;
}
};
src/model/diffusion/lingbot_video.hpp
+11 -11
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@@ -127,17 +127,17 @@ namespace LingBotVideo {
config.topk_group = 2;
config.routed_scaling_factor = 2.5f;
}
LOG_DEBUG("lingbot_video: depth = %" PRId64 ", hidden_size = %" PRId64 ", heads = %" PRId64 ", text_dim = %" PRId64 ", experts = %" PRId64 ", experts_per_tok = %" PRId64 ", n_group = %" PRId64 ", topk_group = %" PRId64 ", route_scale = %.2f, sparse_layers = %zu",
config.depth,
config.hidden_size,
config.num_attention_heads,
config.text_dim,
config.num_experts,
config.num_experts_per_tok,
config.n_group,
config.topk_group,
config.routed_scaling_factor,
config.sparse_layers.size());
LOG_VERBOSE("lingbot_video: depth = %" PRId64 ", hidden_size = %" PRId64 ", heads = %" PRId64 ", text_dim = %" PRId64 ", experts = %" PRId64 ", experts_per_tok = %" PRId64 ", n_group = %" PRId64 ", topk_group = %" PRId64 ", route_scale = %.2f, sparse_layers = %zu",
config.depth,
config.hidden_size,
config.num_attention_heads,
config.text_dim,
config.num_experts,
config.num_experts_per_tok,
config.n_group,
config.topk_group,
config.routed_scaling_factor,
config.sparse_layers.size());
return config;
}
};
src/model/diffusion/ltxv.hpp
+7 -7
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@@ -274,12 +274,12 @@ namespace LTXV {
config.audio_connector_apply_gated_attention = true;
}
}
LOG_DEBUG("ltxav: num_layers = %" PRId64 ", hidden_size = %" PRId64 ", num_attention_heads = %" PRId64 ", audio_hidden_size = %" PRId64 ", audio_num_attention_heads = %" PRId64,
config.num_layers,
config.hidden_size,
config.num_attention_heads,
config.audio_hidden_size,
config.audio_num_attention_heads);
LOG_VERBOSE("ltxav: num_layers = %" PRId64 ", hidden_size = %" PRId64 ", num_attention_heads = %" PRId64 ", audio_hidden_size = %" PRId64 ", audio_num_attention_heads = %" PRId64,
config.num_layers,
config.hidden_size,
config.num_attention_heads,
config.audio_hidden_size,
config.audio_num_attention_heads);
return config;
}
};
@@ -2070,7 +2070,7 @@ namespace LTXV {
GGML_ASSERT(!out_opt.empty());
print_sd_tensor(out_opt, false, "ltxav_out");
LOG_DEBUG("ltxav test done in %lldms", t1 - t0);
LOG_VERBOSE("ltxav test done in %lldms", t1 - t0);
}
static void load_from_file_and_test(const std::string& model_path,
src/model/diffusion/minimax_h3.hpp
+8 -8
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@@ -106,14 +106,14 @@ namespace MiniMaxH3 {
config.rope_inv_freq_len = inv_freq->ne[0];
}
LOG_DEBUG("minimax_h3: layers=%" PRId64 ", hidden=%" PRId64 ", heads=%" PRId64
", head_dim=%" PRId64 ", ffn=%" PRId64 ", adaln_curve=%" PRId64,
config.num_layers,
config.hidden_size,
config.num_attention_heads,
config.attention_head_dim,
config.ffn_hidden_size,
config.adaln_curve_grid);
LOG_VERBOSE("minimax_h3: layers=%" PRId64 ", hidden=%" PRId64 ", heads=%" PRId64
", head_dim=%" PRId64 ", ffn=%" PRId64 ", adaln_curve=%" PRId64,
config.num_layers,
config.hidden_size,
config.num_attention_heads,
config.attention_head_dim,
config.ffn_hidden_size,
config.adaln_curve_grid);
return config;
}
};
src/model/diffusion/minit2i.hpp
+9 -9
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@@ -108,15 +108,15 @@ namespace MiniT2I {
config.head_dim = config.hidden_size == 1248 ? 52 : 64;
config.num_heads = config.hidden_size / config.head_dim;
}
LOG_DEBUG("minit2i: hidden_size=%" PRId64 ", txt_hidden_size=%" PRId64 ", heads=%" PRId64 ", head_dim=%" PRId64 ", double_blocks=%" PRId64 ", txt_blocks=%" PRId64 ", patch=%" PRId64 ", in_channels=%" PRId64,
config.hidden_size,
config.txt_hidden_size,
config.num_heads,
config.head_dim,
config.depth_double,
config.txt_preamble_depth,
config.patch_size,
config.in_channels);
LOG_VERBOSE("minit2i: hidden_size=%" PRId64 ", txt_hidden_size=%" PRId64 ", heads=%" PRId64 ", head_dim=%" PRId64 ", double_blocks=%" PRId64 ", txt_blocks=%" PRId64 ", patch=%" PRId64 ", in_channels=%" PRId64,
config.hidden_size,
config.txt_hidden_size,
config.num_heads,
config.head_dim,
config.depth_double,
config.txt_preamble_depth,
config.patch_size,
config.in_channels);
return config;
}
};
src/model/diffusion/mmdit.hpp
+11 -11
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@@ -120,16 +120,16 @@ struct MMDiTConfig {
}
if (has_weight_config) {
LOG_DEBUG("mmdit: num_layers = %" PRId64 ", num_mmdit_x_layers = %" PRId64 ", hidden_size = %" PRId64 ", patch_size = %d, in_channels = %" PRId64 ", out_channels = %" PRId64 ", context_size = %" PRId64 ", adm_in_channels = %" PRId64 ", qk_norm = %s",
config.depth,
config.d_self + 1,
config.hidden_size,
config.patch_size,
config.in_channels,
config.out_channels,
config.context_size,
config.adm_in_channels,
config.qk_norm.empty() ? "none" : config.qk_norm.c_str());
LOG_VERBOSE("mmdit: num_layers = %" PRId64 ", num_mmdit_x_layers = %" PRId64 ", hidden_size = %" PRId64 ", patch_size = %d, in_channels = %" PRId64 ", out_channels = %" PRId64 ", context_size = %" PRId64 ", adm_in_channels = %" PRId64 ", qk_norm = %s",
config.depth,
config.d_self + 1,
config.hidden_size,
config.patch_size,
config.in_channels,
config.out_channels,
config.context_size,
config.adm_in_channels,
config.qk_norm.empty() ? "none" : config.qk_norm.c_str());
}
return config;
}
@@ -1045,7 +1045,7 @@ struct MMDiTRunner : public DiffusionModelRunner {
GGML_ASSERT(!out_opt.empty());
out = std::move(out_opt);
print_sd_tensor(out);
LOG_DEBUG("mmdit test done in %lldms", t1 - t0);
LOG_VERBOSE("mmdit test done in %lldms", t1 - t0);
}
}
src/model/diffusion/pid.hpp
+10 -10
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@@ -109,16 +109,16 @@ namespace Pid {
config.lq_latent_channels = latent_proj_in_channels;
config.lq_latent_down_factor = latent_proj_in_channels >= 64 ? 16 : 8;
}
LOG_DEBUG("pid: version = %s, patch_depth = %" PRId64 ", pixel_depth = %" PRId64 ", patch_mlp_hidden_dim = %" PRId64 ", lq_latent_channels = %" PRId64 ", lq_hidden_dim = %" PRId64 ", lq_latent_down_factor = %" PRId64 ", lq_latent_unpatchify_factor = %" PRId64 ", lq_interval = %" PRId64,
config.pit_lq_inject ? "1.5" : "1",
config.patch_depth,
config.pixel_depth,
config.patch_mlp_hidden_dim,
config.lq_latent_channels,
config.lq_hidden_dim,
config.lq_latent_down_factor,
config.lq_latent_unpatchify_factor,
config.lq_interval);
LOG_VERBOSE("pid: version = %s, patch_depth = %" PRId64 ", pixel_depth = %" PRId64 ", patch_mlp_hidden_dim = %" PRId64 ", lq_latent_channels = %" PRId64 ", lq_hidden_dim = %" PRId64 ", lq_latent_down_factor = %" PRId64 ", lq_latent_unpatchify_factor = %" PRId64 ", lq_interval = %" PRId64,
config.pit_lq_inject ? "1.5" : "1",
config.patch_depth,
config.pixel_depth,
config.patch_mlp_hidden_dim,
config.lq_latent_channels,
config.lq_hidden_dim,
config.lq_latent_down_factor,
config.lq_latent_unpatchify_factor,
config.lq_interval);
return config;
}
};
src/model/diffusion/qwen_image.hpp
+5 -5
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@@ -49,9 +49,9 @@ namespace Qwen {
}
}
}
LOG_DEBUG("qwen_image: num_layers = %d, zero_cond_t = %s",
config.num_layers,
config.zero_cond_t ? "true" : "false");
LOG_VERBOSE("qwen_image: num_layers = %d, zero_cond_t = %s",
config.num_layers,
config.zero_cond_t ? "true" : "false");
return config;
}
};
@@ -646,7 +646,7 @@ namespace Qwen {
circular_x_enabled,
config.axes_dim);
int pos_len = static_cast<int>(pe_vec.size() / config.axes_dim_sum / 2);
// LOG_DEBUG("pos_len %d", pos_len);
// LOG_VERBOSE("pos_len %d", pos_len);
auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, config.axes_dim_sum / 2, pos_len);
// pe->data = pe_vec.data();
// print_ggml_tensor(pe, true, "pe");
@@ -760,7 +760,7 @@ namespace Qwen {
GGML_ASSERT(!out_opt.empty());
out = std::move(out_opt);
print_sd_tensor(out);
LOG_DEBUG("qwen_image test done in %lldms", t1 - t0);
LOG_VERBOSE("qwen_image test done in %lldms", t1 - t0);
}
}
src/model/diffusion/sefi_image.hpp
+4 -4
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@@ -34,10 +34,10 @@ namespace SefiImage {
config.hidden_size = tensor_storage.ne[1] * 2;
}
}
LOG_DEBUG("sefi_image: semantic_channels = %" PRId64 ", texture_latent_channels = %" PRId64 ", hidden_size = %" PRId64,
config.semantic_channels,
config.texture_latent_channels,
config.hidden_size);
LOG_VERBOSE("sefi_image: semantic_channels = %" PRId64 ", texture_latent_channels = %" PRId64 ", hidden_size = %" PRId64,
config.semantic_channels,
config.texture_latent_channels,
config.hidden_size);
return config;
}
};
src/model/diffusion/unet.hpp
+10 -10
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@@ -128,15 +128,15 @@ struct UNetConfig {
}
}
LOG_DEBUG("unet: in_channels = %d, out_channels = %d, model_channels = %d, time_embed_dim = %d, context_dim = %d, adm_in_channels = %d, num_res_blocks = %d, tiny_unet = %s",
config.in_channels,
config.out_channels,
config.model_channels,
config.time_embed_dim,
config.context_dim,
config.adm_in_channels,
config.num_res_blocks,
config.tiny_unet ? "true" : "false");
LOG_VERBOSE("unet: in_channels = %d, out_channels = %d, model_channels = %d, time_embed_dim = %d, context_dim = %d, adm_in_channels = %d, num_res_blocks = %d, tiny_unet = %s",
config.in_channels,
config.out_channels,
config.model_channels,
config.time_embed_dim,
config.context_dim,
config.adm_in_channels,
config.num_res_blocks,
config.tiny_unet ? "true" : "false");
return config;
}
};
@@ -904,7 +904,7 @@ struct UNetModelRunner : public DiffusionModelRunner {
GGML_ASSERT(!out_opt.empty());
out = std::move(out_opt);
print_sd_tensor(out);
LOG_DEBUG("unet test done in %lldms", t1 - t0);
LOG_VERBOSE("unet test done in %lldms", t1 - t0);
}
}
};
src/model/diffusion/wan.hpp
+9 -9
View File
@@ -75,13 +75,13 @@ namespace WAN {
config.flf_pos_embed_token_number = 514;
}
}
LOG_DEBUG("wan: model_type = %s, num_layers = %d, vace_layers = %d, dim = %" PRId64 ", ffn_dim = %" PRId64 ", num_heads = %" PRId64,
config.model_type.c_str(),
config.num_layers,
config.vace_layers,
config.dim,
config.ffn_dim,
config.num_heads);
LOG_VERBOSE("wan: model_type = %s, num_layers = %d, vace_layers = %d, dim = %" PRId64 ", ffn_dim = %" PRId64 ", num_heads = %" PRId64,
config.model_type.c_str(),
config.num_layers,
config.vace_layers,
config.dim,
config.ffn_dim,
config.num_heads);
return config;
}
};
@@ -909,7 +909,7 @@ namespace WAN {
config.theta,
config.axes_dim);
int pos_len = static_cast<int>(pe_vec.size() / config.axes_dim_sum / 2);
// LOG_DEBUG("pos_len %d", pos_len);
// LOG_VERBOSE("pos_len %d", pos_len);
auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, config.axes_dim_sum / 2, pos_len);
// pe->data = pe_vec.data();
// print_ggml_tensor(pe);
@@ -1007,7 +1007,7 @@ namespace WAN {
GGML_ASSERT(!out_opt.empty());
out = std::move(out_opt);
print_sd_tensor(out);
LOG_DEBUG("wan test done in %lldms", t1 - t0);
LOG_VERBOSE("wan test done in %lldms", t1 - t0);
}
}
src/model/diffusion/z_image.hpp
+10 -10
View File
@@ -107,14 +107,14 @@ namespace ZImage {
config.num_kv_heads = std::max<int64_t>(1, (qkv_heads - config.num_heads) / 2);
}
}
LOG_DEBUG("z_image: num_layers = %" PRId64 ", num_refiner_layers = %" PRId64 ", hidden_size = %" PRId64 ", num_heads = %" PRId64 ", num_kv_heads = %" PRId64 ", in_channels = %" PRId64 ", out_channels = %" PRId64,
config.num_layers,
config.num_refiner_layers,
config.hidden_size,
config.num_heads,
config.num_kv_heads,
config.in_channels,
config.out_channels);
LOG_VERBOSE("z_image: num_layers = %" PRId64 ", num_refiner_layers = %" PRId64 ", hidden_size = %" PRId64 ", num_heads = %" PRId64 ", num_kv_heads = %" PRId64 ", in_channels = %" PRId64 ", out_channels = %" PRId64,
config.num_layers,
config.num_refiner_layers,
config.hidden_size,
config.num_heads,
config.num_kv_heads,
config.in_channels,
config.out_channels);
return config;
}
};
@@ -603,7 +603,7 @@ namespace ZImage {
circular_x_enabled,
config.axes_dim);
int pos_len = static_cast<int>(pe_vec.size() / config.axes_dim_sum / 2);
// LOG_DEBUG("pos_len %d", pos_len);
// LOG_VERBOSE("pos_len %d", pos_len);
auto pe = ggml_new_tensor_4d(compute_ctx, GGML_TYPE_F32, 2, 2, config.axes_dim_sum / 2, pos_len);
// pe->data = pe_vec.data();
// print_ggml_tensor(pe, true, "pe");
@@ -689,7 +689,7 @@ namespace ZImage {
GGML_ASSERT(!out_opt.empty());
out = std::move(out_opt);
print_sd_tensor(out);
LOG_DEBUG("z_image test done in %lldms", t1 - t0);
LOG_VERBOSE("z_image test done in %lldms", t1 - t0);
}
}